All Tools
K
RAGFreeOpen Source
KG_RAG
Ground LLM responses in knowledge graphs for better accuracy
Apache-2.0
ABOUT
Standard RAG retrieves flat text chunks that often lack relational context, leading to shallow or factually incomplete answers. KG_RAG bridges knowledge graphs with LLMs by querying structured graph data first, then feeding the extracted relational context to the LLM. This grounds responses in verifiable entity-relationship triples rather than loose semantic similarity, improving accuracy on domain-specific questions.
INTEGRATION GUIDE
1. Build a biomedical research assistant that answers clinical questions using structured knowledge from ontologies
2. Create an enterprise FAQ system grounded in a company's internal knowledge graph of products, policies, and procedures
3. Enhance a legal document review pipeline by retrieving entity relationships from case law knowledge graphs
4. Power a scientific literature agent that traces concept relationships across papers via graph-based retrieval
5. Deploy a customer support RAG system that resolves queries by traversing product and service relationship graphs
TAGS
pythonragknowledge-graphllmretrievalbiomedicalgraph-rag